Machine Learning With Python Course Online

Machine Learning With Python Course

Python has become the language of choice for machine learning due to its simplicity, versatility, and robust libraries. Machine Learning with Python is a code-writing course providing practical implementation and hands-on development skills, deploying machine learning models with Python's powerful API.

Key aspects of machine learning with Python that we focus on include:

Python's ML Ecosystem: Learn to leverage libraries such as NumPy, Pandas, and Scikit-learn, which form the backbone of machine learning in Python.
Data Manipulation: Master data preprocessing, cleaning, and feature engineering using Pandas and NumPy, essential skills for any machine learning project.
Model Development: Implement a wide range of machine learning algorithms, from basic linear regression to more complicated algorithms such as KNN, Random Forest, Decision tree, etc.
Visualization: Utilize Matplotlib and Seaborn for data visualization and model performance analysis.

In this course, you'll apply what you learn to real-world scenarios such as:

Developing predictive models for business forecasting
Creating recommendation systems for e-commerce platforms
Implementing natural language processing for sentiment analysis
Building image classification systems for automated visual tasks
Designing anomaly detection systems for fraud prevention
Constructing time series models for financial forecasting

By mastering machine learning with Python, you'll be equipped to tackle complex data-driven problems across various domains.

Why Learn Machine Learning with Python

Industry Standard: Python is the most widely used language for machine learning in both industry and academia.
Extensive Libraries: Access a vast ecosystem of libraries and frameworks specifically designed for machine learning tasks.
Rapid Prototyping: Python's simplicity allows for quick implementation and testing of machine learning ideas.
Scalability: Learn to develop models that can scale from small datasets to big data applications.
Versatility: Apply machine learning techniques across various domains, from finance to healthcare to tech.

What You Learn in Our Machine Learning with Python course

Data preprocessing and feature engineering
Supervised learning: regression and classification algorithms
Unsupervised learning: clustering and dimensionality reduction
Ensemble methods and decision trees
Model evaluation, validation, and tuning

Who Should Attend

Software developers interested in adding ML capabilities to their skillset
Data analysts looking to transition into data science roles
Business professionals seeking to leverage ML for decision-making
Researchers wanting to apply ML techniques in their field of study
Students and professionals planning a career in AI and data science

Prerequisites

Basic proficiency in Python programming
Fundamental understanding of statistics and probability
Basic linear algebra and calculus (helpful but not required)
Familiarity with data structures and algorithms

Skills & Techniques

Data cleaning and preparation using Pandas
Implementing machine learning algorithms with Scikit-learn
Visualizing data and results using Matplotlib and Seaborn
Optimizing model performance and hyperparameter tuning
Deploying machine learning models in production environments
Applying machine learning to solve real-world problems

Course Structure

Ch. 1

Introduction to Machine Learning

Ch. 2

Machine Learning Mathematical tools

Ch. 3

Linear Algebra (using Scipy)

Ch. 4

Statistics

Ch. 5

Confusion Matrix, ROC

Ch. 6

Machine Learning Software Tools

Ch. 7

Using Jupyter Notebook

Ch. 8

Model Deployment

Ch. 9

Working with Cloud

Ch. 10

Working with Data-Bases

Ch. 11

Implementing Machine Learning with Python

Ch. 12

Data preprocessing, data exploration

Ch. 13

Data modeling, model evaluation

Ch. 14

Cross Validation

Ch. 15

Decision Trees

Ch. 16

SVM

Ch. 17

Time series, Anomaly Detection

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Meet your instructor

Alex Shoihat

Head of Machine Learning Departments

Alex holds a B.Sc. in Information Systems and an M.A. in Electrical and Electronic Engineering.

As a Machine Learning Engineer at Embedded Academy, Alex specializes in the field of artificial intelligence, applying over 13 years of experience in project development, management, and transitioning from development to production in various domains such as Linux Embedded.

Throughout his career, Alex developed his expertise working with the integration of Machine Learning and Deep Learning in the Computer Vision and Data Analysis field.

What our graduates say

Sarah L.
Machine Learning course graduate

AI/ML developer

The Machine Learning course was super practical, from hardcore algorithms to the ethics of AI. Thanks to the professional connections I met during school got me several job offers to check right after I graduate

Paul N.
Machine Learning course graduate

Machine Learning Engineer

I was initially intimidated by the prospect of learning AI and machine learning because I thought I didn’t have enough mathematical knowledge, but throughout the course my instructor made sure I wouldn’t follow behind. It made a huge difference from other times I experienced with online courses.

Howard E.
AI course graduate

AI Product Manager

As a business leader, the AI/ML program has been instrumental in helping me identify opportunities to leverage intelligent technologies. I've been able to upskill my team and implement data-driven strategies that have given us a significant competitive advantage in the market.

Greg B.
Machine Learning course graduate

Robotics and Automation Specialist

I was impressed by the breadth and depth of the AI/ML curriculum. The hands-on projects, mentorship from industry experts, and exposure to real-world case studies have given me the confidence to apply advanced machine learning methodologies to solve real-world problems.

Leonard H.
AI course graduate

Natural Language Processing (NLP) Specialist

As someone with a background in data analysis, the AI/ML course provided me with the selected skills to take my career forward. I'm now fluent in building and deploying sustainable predictive models that drive strategic decision-making paths.